Adaptive detection and extraction of sparse signals embedded in colored gausssian noise using higher order statistics

R. R. Gharieb, A. Cichocki, S. F. Filipowicz

Research output: Contribution to conferencePaperpeer-review

Abstract

A cumulant-based adaptive approach for the detection and extraction of sparse signal embedded in colored Gaussian noise is presented. In this approach, the extracted signal is obtained by adaptive FIR filtering of the noisy signal. Coefficients of the adaptive filter are updated using a recursive algorithm based on a sum of cumulants of orders k ≥ 3 of the input signal. This is to ensure super sufficient detection of different sparse signals and to ensure efficient removal of colored Gaussian noise. It is shown that when the sparse pulse is absent, the coefficients of the adaptive filter converge to zero. However, when the sparse pulse exists the FIR adaptive filter converges to a type of signal-matched filters. Simulation and experimental results are included to show the high efficiency of the presented approach in comparison with the adaptive short-term correlation counterpart.

Original languageEnglish
Pages631-634
Number of pages4
Publication statusPublished - 2000
Externally publishedYes
EventProceedings of the 10th IEEE Workshop on Statiscal and Array Processing - Pennsylvania, PA, USA
Duration: 14 Aug 200016 Aug 2000

Conference

ConferenceProceedings of the 10th IEEE Workshop on Statiscal and Array Processing
CityPennsylvania, PA, USA
Period14/08/0016/08/00

Fingerprint

Dive into the research topics of 'Adaptive detection and extraction of sparse signals embedded in colored gausssian noise using higher order statistics'. Together they form a unique fingerprint.

Cite this